ICML 2017
434 papers
A Birth-Death Process for Feature Allocation
Konstantina Palla, David Knowles, Zoubin Ghahramani A Closer Look at Memorization in Deep Networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, Simon Lacoste-Julien Accelerating Eulerian Fluid Simulation with Convolutional Networks
Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin Active Heteroscedastic Regression
Kamalika Chaudhuri, Prateek Jain, Nagarajan Natarajan Active Learning for Accurate Estimation of Linear Models
Carlos Riquelme, Mohammad Ghavamzadeh, Alessandro Lazaric Active Learning for Cost-Sensitive Classification
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé, John Langford AdaNet: Adaptive Structural Learning of Artificial Neural Networks
Corinna Cortes, Xavier Gonzalvo, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang Adaptive Consensus ADMM for Distributed Optimization
Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein Adaptive Multiple-Arm Identification
Jiecao Chen, Xi Chen, Qin Zhang, Yuan Zhou Adaptive Neural Networks for Efficient Inference
Tolga Bolukbasi, Joseph Wang, Ofer Dekel, Venkatesh Saligrama Adaptive Sampling Probabilities for Non-Smooth Optimization
Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John C. Duchi Adversarial Feature Matching for Text Generation
Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen, Lawrence Carin Algebraic Variety Models for High-Rank Matrix Completion
Greg Ongie, Rebecca Willett, Robert D. Nowak, Laura Balzano Algorithmic Stability and Hypothesis Complexity
Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao Algorithms for $\ell_p$ Low-Rank Approximation
Flavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar, Silvio Lattanzi, Rina Panigrahy, David P. Woodruff Approximate Steepest Coordinate Descent
Sebastian U. Stich, Anant Raj, Martin Jaggi Asynchronous Stochastic Gradient Descent with Delay Compensation
Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu, Zhi-Ming Ma, Tie-Yan Liu Attentive Recurrent Comparators
Pranav Shyam, Shubham Gupta, Ambedkar Dukkipati Automated Curriculum Learning for Neural Networks
Alex Graves, Marc G. Bellemare, Jacob Menick, Rémi Munos, Koray Kavukcuoglu Axiomatic Attribution for Deep Networks
Mukund Sundararajan, Ankur Taly, Qiqi Yan Bayesian Boolean Matrix Factorisation
Tammo Rukat, Chris C. Holmes, Michalis K. Titsias, Christopher Yau Bayesian Inference on Random Simple Graphs with Power Law Degree Distributions
Juho Lee, Creighton Heaukulani, Zoubin Ghahramani, Lancelot F. James, Seungjin Choi Bayesian Models of Data Streams with Hierarchical Power Priors
Andrés Masegosa, Thomas D. Nielsen, Helge Langseth, Darı́o Ramos-López, Antonio Salmerón, Anders L. Madsen Bayesian Optimization with Tree-Structured Dependencies
Rodolphe Jenatton, Cedric Archambeau, Javier González, Matthias Seeger Being Robust (in High Dimensions) Can Be Practical
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart Boosted Fitted Q-Iteration
Samuele Tosatto, Matteo Pirotta, Carlo D’Eramo, Marcello Restelli Bottleneck Conditional Density Estimation
Rui Shu, Hung H. Bui, Mohammad Ghavamzadeh Breaking Locality Accelerates Block Gauss-Seidel
Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht Canopy Fast Sampling with Cover Trees
Manzil Zaheer, Satwik Kottur, Amr Ahmed, José Moura, Alex Smola Capacity Releasing Diffusion for Speed and Locality
Di Wang, Kimon Fountoulakis, Monika Henzinger, Michael W. Mahoney, Satish Rao Clustering High Dimensional Dynamic Data Streams
Vladimir Braverman, Gereon Frahling, Harry Lang, Christian Sohler, Lin F. Yang Co-Clustering Through Optimal Transport
Charlotte Laclau, Ievgen Redko, Basarab Matei, Younès Bennani, Vincent Brault Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning
Yevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav Sukhatme, Stefan Schaal, Sergey Levine Compressed Sensing Using Generative Models
Ashish Bora, Ajil Jalal, Eric Price, Alexandros G. Dimakis Confident Multiple Choice Learning
Kimin Lee, Changho Hwang, KyoungSoo Park, Jinwoo Shin Consistency Analysis for Binary Classification Revisited
Krzysztof Dembczyński, Wojciech Kotłowski, Oluwasanmi Koyejo, Nagarajan Natarajan Consistent K-Clustering
Silvio Lattanzi, Sergei Vassilvitskii Constrained Policy Optimization
Joshua Achiam, David Held, Aviv Tamar, Pieter Abbeel Contextual Decision Processes with Low Bellman Rank Are PAC-Learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire Convexified Convolutional Neural Networks
Yuchen Zhang, Percy Liang, Martin J. Wainwright Convolutional Sequence to Sequence Learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin Coordinated Multi-Agent Imitation Learning
Hoang M. Le, Yisong Yue, Peter Carr, Patrick Lucey Cost-Optimal Learning of Causal Graphs
Murat Kocaoglu, Alex Dimakis, Sriram Vishwanath Count-Based Exploration with Neural Density Models
Georg Ostrovski, Marc G. Bellemare, Aäron Oord, Rémi Munos Curiosity-Driven Exploration by Self-Supervised Prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, Trevor Darrell Dance Dance Convolution
Chris Donahue, Zachary C. Lipton, Julian McAuley DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
Irina Higgins, Arka Pal, Andrei Rusu, Loic Matthey, Christopher Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, Alexander Lerchner Decoupled Neural Interfaces Using Synthetic Gradients
Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves, David Silver, Koray Kavukcuoglu Deep IV: A Flexible Approach for Counterfactual Prediction
Jason Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy Deep Spectral Clustering Learning
Marc T. Law, Raquel Urtasun, Richard S. Zemel Deep Tensor Convolution on Multicores
David Budden, Alexander Matveev, Shibani Santurkar, Shraman Ray Chaudhuri, Nir Shavit Deep Transfer Learning with Joint Adaptation Networks
Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan Deep Voice: Real-Time Neural Text-to-Speech
Sercan Ö. Arık, Mike Chrzanowski, Adam Coates, Gregory Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Andrew Ng, Jonathan Raiman, Shubho Sengupta, Mohammad Shoeybi Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction
Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell Device Placement Optimization with Reinforcement Learning
Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean Diameter-Based Active Learning
Christopher Tosh, Sanjoy Dasgupta Dictionary Learning Based on Sparse Distribution Tomography
Pedram Pad, Farnood Salehi, Elisa Celis, Patrick Thiran, Michael Unser Differentiable Programs with Neural Libraries
Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow Differentially Private Clustering in High-Dimensional Euclidean Spaces
Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, Hongyang Zhang Distributed Mean Estimation with Limited Communication
Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan Dual Supervised Learning
Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, Tie-Yan Liu Dynamic Word Embeddings
Robert Bamler, Stephan Mandt Efficient Distributed Learning with Sparsity
Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang Efficient Nonmyopic Active Search
Shali Jiang, Gustavo Malkomes, Geoff Converse, Alyssa Shofner, Benjamin Moseley, Roman Garnett Efficient SoftMax Approximation for GPUs
Grave, Armand Joulin, Moustapha Cissé, David Grangier, Hervé Jégou Equivariance Through Parameter-Sharing
Siamak Ravanbakhsh, Jeff Schneider, Barnabás Póczos Exact MAP Inference by Avoiding Fractional Vertices
Erik M. Lindgren, Alexandros G. Dimakis, Adam Klivans Failures of Gradient-Based Deep Learning
Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah Fairness in Reinforcement Learning
Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth Fake News Mitigation via Point Process Based Intervention
Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye, Huan Xu, Rakshit Trivedi, Elias Khalil, Shuang Li, Le Song, Hongyuan Zha FeUdal Networks for Hierarchical Reinforcement Learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu Forward and Reverse Gradient-Based Hyperparameter Optimization
Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil Frame-Based Data Factorizations
Sebastian Mair, Ahcène Boubekki, Ulf Brefeld Gradient Boosted Decision Trees for High Dimensional Sparse Output
Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh Gradient Coding: Avoiding Stragglers in Distributed Learning
Rashish Tandon, Qi Lei, Alexandros G. Dimakis, Nikos Karampatziakis Grammar Variational Autoencoder
Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato How to Escape Saddle Points Efficiently
Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan Identifying Best Interventions Through Online Importance Sampling
Rajat Sen, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai Image-to-Markup Generation with Coarse-to-Fine Attention
Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, Alexander M. Rush Input Convex Neural Networks
Brandon Amos, Lei Xu, J. Zico Kolter Input Switched Affine Networks: An RNN Architecture Designed for Interpretability
Jakob N. Foerster, Justin Gilmer, Jascha Sohl-Dickstein, Jan Chorowski, David Sussillo Interactive Learning from Policy-Dependent Human Feedback
James MacGlashan, Mark K. Ho, Robert Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, Michael L. Littman iSurvive: An Interpretable, Event-Time Prediction Model for mHealth
Walter H. Dempsey, Alexander Moreno, Christy K. Scott, Michael L. Dennis, David H. Gustafson, Susan A. Murphy, James M. Rehg Iterative Machine Teaching
Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda B. Smith, James M. Rehg, Le Song Kernelized Support Tensor Machines
Lifang He, Chun-Ta Lu, Guixiang Ma, Shen Wang, Linlin Shen, Philip S. Yu, Ann B. Ragin Language Modeling with Gated Convolutional Networks
Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier Large-Scale Evolution of Image Classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V. Le, Alexey Kurakin Latent Feature Lasso
Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar Latent Intention Dialogue Models
Tsung-Hsien Wen, Yishu Miao, Phil Blunsom, Steve Young Lazifying Conditional Gradient Algorithms
Gábor Braun, Sebastian Pokutta, Daniel Zink Learned Optimizers That Scale and Generalize
Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gómez Colmenarejo, Misha Denil, Nando Freitas, Jascha Sohl-Dickstein Learning Algorithms for Active Learning
Philip Bachman, Alessandro Sordoni, Adam Trischler Learning Continuous Semantic Representations of Symbolic Expressions
Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton Learning Determinantal Point Processes with Moments and Cycles
John Urschel, Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet Learning in POMDPs with Monte Carlo Tree Search
Sammie Katt, Frans A. Oliehoek, Christopher Amato Learning Latent Space Models with Angular Constraints
Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing Learning to Discover Sparse Graphical Models
Eugene Belilovsky, Kyle Kastner, Gael Varoquaux, Matthew B. Blaschko Learning to Generate Long-Term Future via Hierarchical Prediction
Ruben Villegas, Jimei Yang, Yuliang Zou, Sungryull Sohn, Xunyu Lin, Honglak Lee Learning to Learn Without Gradient Descent by Gradient Descent
Yutian Chen, Matthew W. Hoffman, Sergio Gómez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matt Botvinick, Nando Freitas Local Bayesian Optimization of Motor Skills
Riad Akrour, Dmitry Sorokin, Jan Peters, Gerhard Neumann Logarithmic Time One-Against-Some
Hal Daumé, Nikos Karampatziakis, John Langford, Paul Mineiro Lost Relatives of the Gumbel Trick
Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller Magnetic Hamiltonian Monte Carlo
Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard Turner Maximum Selection and Ranking Under Noisy Comparisons
Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati, Ananda Theertha Suresh Meta Networks
Tsendsuren Munkhdalai, Hong Yu Minimax Regret Bounds for Reinforcement Learning
Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos Minimizing Trust Leaks for Robust Sybil Detection
János Höner, Shinichi Nakajima, Alexander Bauer, Klaus-Robert Müller, Nico Görnitz Model-Independent Online Learning for Influence Maximization
Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt Multi-Fidelity Bayesian Optimisation with Continuous Approximations
Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, Barnabás Póczos Multichannel End-to-End Speech Recognition
Tsubasa Ochiai, Shinji Watanabe, Takaaki Hori, John R. Hershey Multilevel Clustering via Wasserstein Means
Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Phung Multiple Clustering Views from Multiple Uncertain Experts
Yale Chang, Junxiang Chen, Michael H. Cho, Peter J. Castaldi, Edwin K. Silverman, Jennifer G. Dy Nearly Optimal Robust Matrix Completion
Yeshwanth Cherapanamjeri, Kartik Gupta, Prateek Jain Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders
Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Mohammad Norouzi, Douglas Eck, Karen Simonyan Neural Episodic Control
Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech Badia, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell Neural Message Passing for Quantum Chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl Neural Optimizer Search with Reinforcement Learning
Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc V. Le On Approximation Guarantees for Greedy Low Rank Optimization
Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Joydeep Ghosh, Sahand Negahban On Calibration of Modern Neural Networks
Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger On Context-Dependent Clustering of Bandits
Claudio Gentile, Shuai Li, Purushottam Kar, Alexandros Karatzoglou, Giovanni Zappella, Evans Etrue On Kernelized Multi-Armed Bandits
Sayak Ray Chowdhury, Aditya Gopalan On the Expressive Power of Deep Neural Networks
Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein On the Projection Operator to a Three-View Cardinality Constrained Set
Haichuan Yang, Shupeng Gui, Chuyang Ke, Daniel Stefankovic, Ryohei Fujimaki, Ji Liu On the Sampling Problem for Kernel Quadrature
François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark Girolami Online and Linear-Time Attention by Enforcing Monotonic Alignments
Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck Online Learning to Rank in Stochastic Click Models
Masrour Zoghi, Tomas Tunys, Mohammad Ghavamzadeh, Branislav Kveton, Csaba Szepesvari, Zheng Wen Parallel Multiscale Autoregressive Density Estimation
Scott Reed, Aäron Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Yutian Chen, Dan Belov, Nando Freitas Parseval Networks: Improving Robustness to Adversarial Examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, Nicolas Usunier Post-Inference Prior Swapping
Willie Neiswanger, Eric Xing Preferential Bayesian Optimization
Javier González, Zhenwen Dai, Andreas Damianou, Neil D. Lawrence Probabilistic Path Hamiltonian Monte Carlo
Vu Dinh, Arman Bilge, Cheng Zhang, Frederick A. Matsen IV Probabilistic Submodular Maximization in Sub-Linear Time
Serban Stan, Morteza Zadimoghaddam, Andreas Krause, Amin Karbasi Programming with a Differentiable Forth Interpreter
Matko Bošnjak, Tim Rocktäschel, Jason Naradowsky, Sebastian Riedel Projection-Free Distributed Online Learning in Networks
Wenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, Tong Zhang ProtoNN: Compressed and Accurate kNN for Resource-Scarce Devices
Chirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, Prateek Jain Random Feature Expansions for Deep Gaussian Processes
Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone Recovery Guarantees for One-Hidden-Layer Neural Networks
Kai Zhong, Zhao Song, Prateek Jain, Peter L. Bartlett, Inderjit S. Dhillon Recurrent Highway Networks
Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutnı́k, Jürgen Schmidhuber Reinforcement Learning with Deep Energy-Based Policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, Sergey Levine Robust Adversarial Reinforcement Learning
Lerrel Pinto, James Davidson, Rahul Sukthankar, Abhinav Gupta Robust Submodular Maximization: A Non-Uniform Partitioning Approach
Ilija Bogunovic, Slobodan Mitrović, Jonathan Scarlett, Volkan Cevher RobustFill: Neural Program Learning Under Noisy I/O
Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli Scalable Bayesian Rule Lists
Hongyu Yang, Cynthia Rudin, Margo Seltzer Schema Networks: Zero-Shot Transfer with a Generative Causal Model of Intuitive Physics
Ken Kansky, Tom Silver, David A. Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, Scott Phoenix, Dileep George Selective Inference for Sparse High-Order Interaction Models
Shinya Suzumura, Kazuya Nakagawa, Yuta Umezu, Koji Tsuda, Ichiro Takeuchi Self-Paced Co-Training
Fan Ma, Deyu Meng, Qi Xie, Zina Li, Xuanyi Dong Sequence Modeling via Segmentations
Chong Wang, Yining Wang, Po-Sen Huang, Abdelrahman Mohamed, Dengyong Zhou, Li Deng Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-Control
Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck Sharp Minima Can Generalize for Deep Nets
Laurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
Jakob Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip H. S. Torr, Pushmeet Kohli, Shimon Whiteson Stochastic Bouncy Particle Sampler
Ari Pakman, Dar Gilboa, David Carlson, Liam Paninski Stochastic Generative Hashing
Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song Stochastic Gradient Monomial Gamma Sampler
Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, Lawrence Carin Stochastic Variance Reduction Methods for Policy Evaluation
Simon S. Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou Tensor Balancing on Statistical Manifold
Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda Tensor Belief Propagation
Andrew Wrigley, Wee Sun Lee, Nan Ye The Predictron: End-to-End Learning and Planning
David Silver, Hado Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris The Shattered Gradients Problem: If Resnets Are the Answer, Then What Is the Question?
David Balduzzi, Marcus Frean, Lennox Leary, J. P. Lewis, Kurt Wan-Duo Ma, Brian McWilliams The Statistical Recurrent Unit
Junier B. Oliva, Barnabás Póczos, Jeff Schneider Tight Bounds for Approximate Carathéodory and Beyond
Vahab Mirrokni, Renato Paes Leme, Adrian Vladu, Sam Chiu-wai Wong Toward Controlled Generation of Text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing Tunable Efficient Unitary Neural Networks (EUNN) and Their Application to RNNs
Li Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott Skirlo, Yann LeCun, Max Tegmark, Marin Soljačić Uncovering Causality from Multivariate Hawkes Integrated Cumulants
Massil Achab, Emmanuel Bacry, Stéphane Gaı̈ffas, Iacopo Mastromatteo, Jean-François Muzy Understanding Synthetic Gradients and Decoupled Neural Interfaces
Wojciech Marian Czarnecki, Grzegorz Świrszcz, Max Jaderberg, Simon Osindero, Oriol Vinyals, Koray Kavukcuoglu Uniform Deviation Bounds for K-Means Clustering
Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause Variational Policy for Guiding Point Processes
Yichen Wang, Grady Williams, Evangelos Theodorou, Le Song Video Pixel Networks
Nal Kalchbrenner, Aäron Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, Koray Kavukcuoglu Wasserstein Generative Adversarial Networks
Martin Arjovsky, Soumith Chintala, Léon Bottou World of Bits: An Open-Domain Platform for Web-Based Agents
Tianlin Shi, Andrej Karpathy, Linxi Fan, Jonathan Hernandez, Percy Liang